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      Advances in integrated weed management 

      Modelling the effects of cropping systems on weed dynamics: the trade-off between process analysis and decision support

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          Abstract

          Models are essential to synthesize knowledge on weeds and to design integrated weed-management strategies. These models must rank cropping systems as a function of weed infestation, and account for variability in effects to estimate probabilities of success or failure. Three case studies are presented: (1) an empirical static single-equation model that directly relates weed biomass to crop management, with few inputs and parameters, (2) a matrix-based multiannual model predicting a few key weed stages annually, from weed control options and a few parameters, (3) a mechanistic process-based multiannual model predicting detailed soil, crop and weed state variables daily, with an individual-based 3D canopy representation, requiring hundreds of inputs and parameters. The chapter concludes that models using a mechanistic representation of the cropping-system ´ environment interactions are best for quantifying effects and their variability, combined with a subsequent transformation with in silico experiments into empirical models of key cropping-system components.

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          Author and book information

          Book Chapter
          9781801464826
          25 April 2022
          Affiliations
          [1 ]AgroSup Dijon, INRAE, Université de Bourgogne (France)
          10.19103/AS.2021.0098.07
          b8800389-65df-4f94-9af2-972b50089e7e
          History

          TECHNOLOGY & ENGINEERING / Pest Control,TECHNOLOGY & ENGINEERING / Agriculture / Sustainable Agriculture,TECHNOLOGY & ENGINEERING / Agriculture / Agronomy / Crop Science,Pest control / plant diseases,Sustainable agriculture,Agronomy and crop production,Pest control,Sustainable agriculture,Agronomy & crop production

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